[1]} used a model discrepancy term when developing a Bayesian approach to calibrate a numerical model that gives a binary output: ice presence or absence. Their method was used to obtain posterior distributions for a variety of physically relevant parameters (including calving factor, basal sliding coefficient, and asthenospheric relaxation \(e\) -folding time), as well as to make projections in ice volume changes, and consequently predictions of sea-level rise, using the PSU3D-ICE model [2]} and data on the Amundsen Sea Embayment in West Antarctica. In addition to model discrepancy, [1]} also included an emulator/surrogate model in order to reduce the computational time needed when working with a computationally expensive computer model, much in the spirit of [4]}. Emulators are designed to mimic the output of the computer model, but are much less computationally intensive. Inference was made in two steps: First, an emulator for the computer model was constructed using Gaussian processes. Then, the numerical model was calibrated using the emulator and a model discrepancy term, also modelled as a Gaussian process. The work of [1]} and that of [6]} are among the few that use discrepancy models in a glaciological context.
